docs: def mahalanobis_norm(self, dx):
          """compute the Mahalanobis norm that is induced by the adapted
          sample distribution, covariance matrix ``C`` times ``sigma**2``,
          including ``sigma_vec``. The expected Mahalanobis distance to
          the sample mean is about ``sqrt(dimension)``.
  
          Argument
          --------
          A *genotype* difference `dx`.
  
          Example
          -------
          >>> import cma, numpy
          >>> es = cma.CMAEvolutionStrategy(numpy.ones(10), 1)
          >>> xx = numpy.random.randn(2, 10)
          >>> d = es.mahalanobis_norm(es.gp.geno(xx[0]-xx[1]))
  
          `d` is the distance "in" the true sample distribution,
          sampled points have a typical distance of ``sqrt(2*es.N)``,
          where ``es.N`` is the dimension, and an expected distance of
          close to ``sqrt(N)`` to the sample mean. In the example,
          `d` is the Euclidean distance, because C = I and sigma = 1.
  
          """
          return sqrt(sum((self.D**-1. * np.dot(self.B.T, dx / self.sigma_vec))**2)) / self.sigma